The prevalence, characteristics and effectiveness of Aichi Target 11 ' s "other effective area-based conservation measures" (OECMs) in Key Biodiversity Areas

Aichi Target 11 of the CBD Strategic Plan for Biodiversity commits countries to the effective conservation of areas of importance for biodiversity, through protected areas and “other effective area-based conservation measures” (OECMs). However, the prevalence and characteristics of OECMs are poorly known, particularly in sites of importance for biodiversity. We assess the prevalence of potential OECMs in 740 terrestrial Key Biodiversity Areas (KBAs) outside known or mapped protected areas across ten countries. A majority of unprotected KBAs (76.5%) were at least partly covered by one or more potential OECMs. The conservation of ecosystem services or biodiversity was a stated management aim in 73% of these OECMs. Local or central government bodies managed the highest number of potential OECMs, followed by local and indigenous communities and private landowners. There was no difference between unprotected KBAs with or without OECMs in forest loss or in a number of state-pressure-response metrics.The project was funded by the CCI Collaborative Fun

The prevalence, characteristics and effectiveness of Aichi Target 11's "other effective area‐based conservation measures" (OECMs) in key biodiversity areas

Aichi Target 11 of the CBD Strategic Plan for Biodiversity commits countries to the effective conservation of areas of importance for biodiversity, through protected areas and "other effective area-based conservation measures" (OECMs). However, the prevalence and characteristics of OECMs are poorly known, particularly in sites of importance for biodiversity. We assess the prevalence of potential OECMs in 740 terrestrial Key Biodiversity Areas (KBAs) outside known or mapped protected areas across ten countries. A majority of unprotected KBAs (76.5%) were at least partly covered by one or more potential OECMs. The conservation of ecosystem services or biodiversity was a stated management aim in 73% of these OECMs. Local or central government bodies managed the highest number of potential OECMs, followed by local and indigenous communities and private landowners. There was no difference between unprotected KBAs with or without OECMs in forest loss or in a number of state-pressure-response metrics

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species’ population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate ‘intactness scores’: the remaining proportion of an ‘intact’ reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region’s major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/ taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species’ population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate ‘intactness scores’: the remaining proportion of an ‘intact’ reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region’s major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

International audienceSub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species' population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate 'intactness scores': the remaining proportion of an 'intact' reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region's major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/ taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

bi4africa dataset - open source

The bii4africa dataset is presented in a multi-spreadsheet .ods file. The raw data spreadsheet (‘Scores_Raw’) includes 31,313 individual expert estimates of the impact of a sub-Saharan African land use on a species response group of terrestrial vertebrates or vascular plants. Estimates are reported as intactness scores - the remaining proportion of an ‘intact’ reference (pre-industrial or contemporary wilderness area) population of a species response group in a land use, on a scale from 0 (no individuals remain) through 0.5 (half the individuals remain), to 1 (same as the reference population) and, in limited cases, to 2 (two or more times the reference population). For species that thrive in human-modified landscapes, scores could be greater than 1 but not exceeding 2 to avoid extremely large scores biasing aggregation exercises. Expert comments are included alongside respective estimates

bii4africa dataset

The bii4africa dataset is presented in a multi-spreadsheet .xlsx file. The raw data spreadsheet (‘Scores_Raw’) includes 31,313 individual expert estimates of the impact of a sub-Saharan African land use on a species response group of terrestrial vertebrates or vascular plants. Estimates are reported as intactness scores - the remaining proportion of an ‘intact’ reference (pre-industrial or contemporary wilderness area) population of a species response group in a land use, on a scale from 0 (no individuals remain) through 0.5 (half the individuals remain), to 1 (same as the reference population) and, in limited cases, to 2 (two or more times the reference population). For species that thrive in human-modified landscapes, scores could be greater than 1 but not exceeding 2 to avoid extremely large scores biasing aggregation exercises. Expert comments are included alongside respective estimates